Latest activity
BioStar bookmarked MPXV Bookmarks- BioStar created a new bio-script Identify genome-wide synteny with LASTZ alignment
- Jitendra Narayan is now a friend with Devinda
- Devinda is now a friend with Jitendra Narayan
- RASALSI published a blog post THE LIMITATIONS OF ALPHAFOLD2
- BioStar bookmarked OneZoom tree of life explorer...
- Abhinav published a blog post Interesting Bioinformatics Resources !
- Abhinav published a blog post Tools for Differential expression analysisComments
- LEGE 432 days ago
Differential expression analysis is a widely used technique in genomics and transcriptomics research that allows researchers to identify genes that are differentially expressed between two or more conditions or groups. There are several tools available for performing differential expression analysis, including:
-
DESeq2: This R package is widely used for analyzing RNA-Seq data. It uses a negative binomial distribution to model the count data and provides methods for estimating variance and testing for differential expression.
-
edgeR: Another R package that is commonly used for differential expression analysis of RNA-Seq data. It uses a generalized linear model to model the count data and provides methods for estimating dispersion and testing for differential expression.
-
limma: A popular R package that is used for differential expression analysis of microarray and RNA-Seq data. It uses a linear model to model the gene expression data and provides methods for estimating variance and testing for differential expression.
-
NOISeq: This is an R package that is used for differential expression analysis of RNA-Seq data. It uses a non-parametric method based on the relative expression values of the genes and provides a user-friendly interface for performing the analysis.
-
Cuffdiff: This tool is a part of the Cufflinks suite and is used for differential expression analysis of RNA-Seq data. It uses a Bayesian framework to model the gene expression data and provides methods for estimating variance and testing for differential expression.
-
DEGSeq: This R package is used for differential expression analysis of RNA-Seq data. It uses a method based on the negative binomial distribution to model the count data and provides methods for estimating variance and testing for differential expression.
These are just a few examples of the many tools available for differential expression analysis. The choice of tool depends on the specific research question, the type of data, and the user's familiarity with the tool.
-
- Abhinav published a blog post Tools for RNA classificationComments
- LEGE 432 days ago
There are several tools available for RNA classification, each with its own strengths and limitations. Here are some commonly used tools:
-
Infernal: Infernal is a popular tool for RNA classification that uses a covariance model approach to identify RNA homologs. It is particularly useful for identifying non-coding RNA (ncRNA) sequences.
-
Rfam: Rfam is a database of RNA families and their corresponding covariance models. It is based on Infernal and provides a comprehensive resource for RNA classification.
-
RNAcode: RNAcode is a machine learning-based tool that uses a support vector machine (SVM) algorithm to classify RNA sequences. It is particularly useful for identifying small functional RNA molecules.
-
RNAmmer: RNAmmer is a tool for predicting rRNA genes in genomic sequences. It uses a combination of HMM-based and BLAST-based approaches to identify rRNA sequences.
-
tRNAscan-SE: tRNAscan-SE is a tool for identifying tRNA genes in genomic sequences. It uses a combination of HMM-based and comparative sequence analysis approaches to predict tRNA genes.
These tools can be used individually or in combination to achieve the best possible classification of RNA sequences.
-
- Abhinav published a blog post Tools for Sequence translation !Comments
- LEGE 432 days ago
Sequence translation is the process of converting a DNA or RNA sequence into its corresponding protein sequence. This is an important step in the analysis of genomic and transcriptomic data. There are several tools available for sequence translation, including:
-
ExPASy Translate Tool: This is a web-based tool that allows users to translate a DNA sequence into its corresponding protein sequence. It supports several genetic codes and can handle multiple sequences at once.
-
EMBOSS Transeq: This is a command-line tool that can translate nucleotide sequences into amino acid sequences. It supports several genetic codes and can also perform reverse translation (i.e., convert a protein sequence into its corresponding nucleotide sequence).
-
BioPython: This is a Python library that provides several tools for bioinformatics analysis, including sequence translation. It supports several genetic codes and provides functions for translating DNA or RNA sequences into protein sequences.
-
SeqKit: This is a command-line tool that can perform several sequence manipulations, including sequence translation. It supports several genetic codes and can handle multiple sequences at once.
-
CLC Sequence Viewer: This is a desktop application that provides several tools for sequence analysis, including sequence translation. It supports several genetic codes and provides a user-friendly interface for performing the analysis.
These are just a few examples of the many tools available for sequence translation. The choice of tool depends on the specific requirements of the user, including the input format, the genetic code used, and the type of output required.
-
- Abhinav posted a new ad in the Opportunity PhD positions on integrative omics and phylogenomics
- Abhinav created a new bio-script Perl script to read the next line of a file !
